Esselman Allison B, Moser Felipe A, Tideman Léonore E M, Migas Lukasz G, Djambazova Katerina V, Colley Madeline E, Pingry Ellie L, Patterson Nathan Heath, Farrow Melissa A, Yang Haichun, Fogo Agnes B, de Caestecker Mark, Van de Plas Raf, Spraggins Jeffrey M
Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA; Department of Chemistry, Vanderbilt University, Nashville, Tennessee, USA.
Delft Center for Systems and Control, Delft University of Technology, Delft, the Netherlands.
Kidney Int. 2025 Feb;107(2):332-337. doi: 10.1016/j.kint.2024.11.008. Epub 2024 Nov 20.
Glomeruli filter blood through the coordination of podocytes, mesangial cells, fenestrated endothelial cells, and the glomerular basement membrane. Cellular changes, such as podocyte loss, are associated with pathologies like diabetic kidney disease. However, little is known regarding the in situ molecular profiles of specific cell types and how these profiles change with disease. Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is well-suited for untargeted tissue mapping of a wide range of molecular classes. Importantly, additional imaging modalities can be integrated with MALDI IMS to associate these biomolecular distributions to specific cell types. Here, we integrated workflow combining MALDI IMS and multiplexed immunofluorescence (MxIF) microscopy. High spatial resolution MALDI IMS (5 μm) was used to determine lipid distributions within human glomeruli from a normal portion of fresh-frozen kidney cancer nephrectomy tissue revealing intra-glomerular lipid heterogeneity. Mass spectrometric data were linked to specific glomerular cell types and substructures through new methods that enable MxIF microscopy to be performed on the same tissue section following MALDI IMS, without sacrificing signal quality from either modality. Machine learning approaches were combined enabling cell type segmentation and identification based on MxIF data. This was followed by mining of cell type or cluster-associated MALDI IMS signatures using classification and interpretable machine learning. This allowed automated discovery of spatially specific molecular markers for glomerular cell types and substructures as well as lipids correlated to deep and superficial glomeruli. Overall, our work establishes a toolbox for probing molecular signatures of glomerular cell types and substructures within tissue microenvironments providing a framework applicable to other kidney tissue features and organ systems.
肾小球通过足细胞、系膜细胞、有窗孔的内皮细胞和肾小球基底膜的协同作用过滤血液。细胞变化，如足细胞丢失，与糖尿病肾病等病理状况相关。然而，关于特定细胞类型的原位分子谱以及这些谱如何随疾病变化，我们知之甚少。基质辅助激光解吸/电离成像质谱（MALDI IMS）非常适合对广泛分子类别的非靶向组织图谱分析。重要的是，其他成像方式可以与MALDI IMS整合，将这些生物分子分布与特定细胞类型相关联。在这里，我们整合了结合MALDI IMS和多重免疫荧光（MxIF）显微镜的工作流程。使用高空间分辨率MALDI IMS（5μm）来确定来自新鲜冷冻肾癌肾切除组织正常部分的人肾小球内的脂质分布，揭示肾小球内脂质的异质性。通过新方法将质谱数据与特定的肾小球细胞类型和亚结构联系起来，这些方法使MxIF显微镜能够在MALDI IMS之后对同一组织切片进行，而不会牺牲任何一种方式的信号质量。结合机器学习方法，能够基于MxIF数据进行细胞类型分割和识别。随后，使用分类和可解释机器学习挖掘细胞类型或簇相关的MALDI IMS特征。这允许自动发现肾小球细胞类型和亚结构以及与深层和浅层肾小球相关的脂质的空间特异性分子标记。总体而言，我们的工作建立了一个工具箱，用于探测组织微环境中肾小球细胞类型和亚结构的分子特征，提供了一个适用于其他肾脏组织特征和器官系统的框架。
